Data labeling in machine learning is the process of classifying unlabeled data (such as photos, text files, videos, etc.) and adding one or more insightful labels to give the data context so that a ML model can be trained by it.
The market share leader is the text category. Use examples include sentiment tagging, in which people assign text the emotion (such as anger, happiness, etc.) it expresses.
These techniques enable machine reading of images through image labeling.
Bounding boxes, polygonal segmentation, line annotation, landmark annotation, 3D cuboids, semantic segmentation, and other forms of image labeling approaches are only a few examples.
This includes labeling for audio and video.
Businesses are adopting AI technology to automate decision-making and benefit from new business opportunities, but it is not as easy as it seems and data annotation is the most challenging limitation to AI adoption in the industry. Data labeling enables machines to gain an accurate understanding of real-world conditions and opens up opportunities for a wide variety of businesses and industries. Having better-labeled data than competitors provides superiority in the machine learning industry.
1. Internal costs are impractical or unsustainable:
In advanced economies with high worker wages, labeling data internally is particularly expensive. These expenditures can increase to the point where it is no longer practical to continue labeling in-house for larger and larger datasets.
2. Unexpected delays:
When working with an internal team, overall performance may suffer due to different reasons like change of roles, need for training or reallocation of resources. Contractual agreements that state that data will be given at particular intervals and with an acceptable quality level can guarantee delivery dates when outsourcing to a trustworthy third-party provider.
3. Difficulty in recruiting and training labelers:
It's not always possible to hire new labelers if your internal labeling team has decreased in size or is not big enough. This is because new personnel need training in order to produce labels of a high enough quality.
4. Annotators may lack knowledge of certain industries:
Some industries may not be well-known to annotators. Fields like finance and healthcare require a certain level of subject-matter proficiency from the labelers carrying out the annotation. The project might be better served by collaborating with a labeling company whose data annotators have industry-specific capabilities in cases when the in-house labelers lack these abilities and there are few chances for recruitment.
5. Biases in annotation:
By using an in-house annotator team, you can generate some bias in the annotation. Indeed, if your team is composed of people who have the same physical attributes and have the same origin, you can reproduce certain social biases. In this case, your internal team will have only one reading prism and will not be able to provide the most complete learning to your algorithm. By choosing a diverse team of annotators, coming from different countries and cultures, you reduce the bias and provide the most accurate learning to your model.
Large amounts of high-quality training data serve as the basis for effective machine learning models. However, the process of collecting the training data needed to develop these models is difficult and time consuming. The most common models today require that the data be manually labeled by humans in order for the models to learn to make good decisions.
Annotating in-house can limit you in terms of volume and create some bias in the annotation.
Today, companies specialized in data labeling can make all the difference in training your algorithms: by training and coaching a diverse and committed workforce, with a project team that follows the quality of the annotations and monitors your projects daily. Moreover, outsourcing your annotations can also be an opportunity for the company to generate a positive social impact with annotators, by using a partner like isahit, which guarantees extremely accurate annotations but also a 5x higher income for annotators, free training, and a friendly community to rely on.
Check out our article on how to choose the best data labeling partner your projects for more tips.
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